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Machine Learning Operations (MLOps)
Getting Started - 日本語版
Google Cloud Training
Google Cloud Training
このコースでは、Google Cloud 上で本番環境の ML システムをデプロイ、評価、モニタリング、運用するための MLOps ツールとベスト プラクティスについて説明します。MLOps は、本番環境 ML システムのデプロイ、テスト、モニタリング、自動化に重点を置いた規範です。機械学習エンジニアリングの担当者は、ツールを活用して、デプロイしたモデルの継続的な改善と評価を行います。また、データ サイエンティストと協力して、あるいは自らがデータ サイエンティストとして、最も効果的なモデルを迅速かつ正確にデプロイできるようモデルを開発します。
Register for this course and see more details by visiting:
OpenCourser.com/course/c4opuz/mlops
What's inside
Syllabus
機械学習オペレーション（MLOps）へようこそ: スタートガイド
このモジュールでは、コースの概要を説明します
ML オペレーションの採用
ML 担当者の課題
ML における DevOps のコンセプト
ML のライフサイクルの 3 つのフェーズ
ML プロセスの自動化
Read more
Syllabus
機械学習オペレーション（MLOps）へようこそ: スタートガイド
このモジュールでは、コースの概要を説明します
ML オペレーションの採用
ML 担当者の課題
ML における DevOps のコンセプト
ML のライフサイクルの 3 つのフェーズ
ML プロセスの自動化
Read more
Vertex AI と、Vertex AI での MLOps
Vertex AI の概要と、統合プラットフォームが重要な理由
Vertex AI での MLOps の概要
Vertex AI による MLOps ワークフローの支援パート 1
Vertex AI による MLOps ワークフローの支援パート 2
まとめ
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
機械学習オペレーターを対象に開発されており、機械学習エンジニアリングやデータサイエンスの職種のスキル強化に適しています。
実務で活用できるMLOpsツールやベストプラクティスを紹介しており、エンジニアの業務効率化に貢献します。
Google Cloud Trainingが講師を務めており、信頼性の高い学習機会を提供できます。
Vertex AIの活用方法を体系的に学べ、実践的なスキルを身につけるのに役立ちます。
MLシステムのデプロイ、評価、モニタリング、運用の自動化に関する包括的な内容をカバーしています。
前提条件が特に記載されていないため、初心者でも受講しやすいです。
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Activities
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We're preparing activities for
Machine Learning Operations (MLOps): Getting Started - 日本語版.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Machine Learning Operations (MLOps): Getting Started - 日本語版 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers improve systems, products, and services with AI. In this role, you would apply your Machine Learning skills to create profitable business outcomes by translating ML models into production-ready code. This course's introduction to MLOps tools and best practices would help prepare you to deploy, evaluate, monitor, and iterate on deployed models. This knowledge would help you continuously improve the quality of your solutions and, ultimately, accelerate results for your team.
Data Scientist
Data Scientist
Data Scientists solve complex business problems using AI. In this role, you would need the skills to translate business problems into ML solutions. This course helps Data Scientists understand how to deploy and maintain their models as part of a real-world ML system.
Software Engineer
Software Engineer
Software Engineers design, develop, deploy, and maintain software systems. As an ML-focused Software Engineer, you would apply your knowledge of MLOps tools and best practices to the development of production-ready ML solutions. This course's introduction to Vertex AI would be especially helpful in getting you up to speed on platforms designed to streamline the ML development process.
DevOps Engineer
DevOps Engineer
DevOps Engineers focus on improving communication and collaboration between development and operations teams. Those in this role are responsible for the deployment, operation, and maintenance of software systems. This course provides an introduction to the DevOps concepts behind the ML lifecycle, which would be useful knowledge for anyone looking to work as a DevOps Engineer in a machine learning environment.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and maintain cloud computing systems. Some Cloud Engineers specialize in supporting the needs of Data Scientists and Machine Learning Engineers. This course provides an introduction to Google Cloud's Vertex AI platform, which is one of the leading platforms for the deployment and management of ML models.
Data Analyst
Data Analyst
Data Analysts collect, clean, analyze, and interpret data. They work closely with Data Scientists and Machine Learning Engineers to transform raw information into actionable insights. This course may be useful for Data Analysts looking to gain experience with the basics of MLOps, which is becoming an increasingly important skill for those working with ML systems.
Business Analyst
Business Analyst
Business Analysts bridge the gap between technical teams and business stakeholders. They play a key role in defining and prioritizing business requirements. Those in this role may find it useful to understand the basics of MLOps, which helps ensure that ML models are aligned with business objectives.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products or features. They work closely with engineering, design, and marketing teams to ensure that products meet the needs of users. This course may be useful for Product Managers who are looking to gain a better understanding of the technical aspects of ML model deployment and management.
Technical Writer
Technical Writer
Technical Writers create documentation for software and hardware products. This course may be of interest to Technical Writers looking to gain experience with MLOps-related topics, which are becoming increasingly important in the field of technical writing.
Educator
Educator
Educators teach students about a variety of subjects. Some Educators specialize in teaching computer science and data science. Those in this role may find it useful to gain experience with MLOps, which is an increasingly important topic in the field of data science. This course provides an introduction to MLOps tools and best practices, as well as an overview of the ML lifecycle and the role of DevOps in ML.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. Those who specialize in data science or machine learning may find it useful to gain experience with MLOps, which is an increasingly important topic in the field of data science.
Salesperson
Salesperson
Salespeople sell products and services to businesses and consumers. Those who specialize in selling software or data science products may find it useful to gain experience with MLOps, which is an increasingly important topic in the field of data science.
Marketer
Marketer
Marketers develop and execute marketing campaigns to promote products and services. Those who specialize in marketing software or data science products may find it useful to gain experience with MLOps, which is an increasingly important topic in the field of data science.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers get the most value from their products and services. Those who work with customers in the software or data science industries may find it useful to gain experience with MLOps, which is an increasingly important topic in the field of data science.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. Those who work in businesses that use software or data science may find it useful to gain experience with MLOps, which is an increasingly important topic in the field of data science.
For more career information including salaries, visit:
OpenCourser.com/course/c4opuz/mlops
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning Operations (MLOps): Getting Started - 日本語版.
Save
MLOps に関連するデータ処理とアーキテクチャの重要な側面を探ります。分散システム、ストリーミングデータ、NoSQL データベースに関する基礎を提供します。
MLOps に使用される高度な機械学習手法を探ります。強化学習、自然言語処理、時系列分析などのトピックに関する理論と応用の両方をカバーします。
MLOps のビジネス側面を探ります。機械学習プロジェクトの価値を測定し、ビジネス上の意思決定にMLインサイトを統合するための手法を扱います。
この書籍は、ML の実践的な側面に焦点を当てています。モデルのトレーニング、評価、最適化に関する包括的なリソースです。
この書籍は、Python を使用した ML の入門書です。機械学習の概念、アルゴリズム、実装に関する分かりやすい説明を提供します。
機械学習モデルの構築と評価に関する実践的なガイドを提供します。Scikit-Learn、Keras、TensorFlow などの主要な ML ライブラリの使用に関する知識を深めることができます。
この書籍は、Keras ライブラリを使用したディープラーニングの包括的なガイドです。ニューラルネットワークの設計、トレーニング、評価に関する詳細な情報を提供しています。
MLOps で広く使用されている Python ライブラリである Scikit-Learn、Keras、TensorFlow に関する実践的なガイドを提供します。これらのライブラリの使用方法に関する実践的な知識を習得できます。
この書籍は、確率的観点から機械学習を扱っています。ベイズ推論、モデル選択、時系列解析に関する高度なトピックをカバーしています。
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c4opuz/mlops
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Effort
3 weeks of study, 6-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
日本語
Good to know
機械学習オペレーターを対象に開発されており、機械学習エンジニアリングやデータサイエンスの職種のスキル強化に適しています。
実務で活用できるMLOpsツールやベストプラクティスを紹介しており、エンジニアの業務効率化に貢献します。
Google Cloud Trainingが講師を務めており、信頼性の高い学習機会を提供できます。
Vertex AIの活用方法を体系的に学べ、実践的なスキルを身につけるのに役立ちます。
MLシステムのデプロイ、評価、モニタリング、運用の自動化に関する包括的な内容をカバーしています。
前提条件が特に記載されていないため、初心者でも受講しやすいです。
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